Cell Counts

library(tidyverse)
library(scales)
library(ggtext)

File Paths

# Folder paths
MOC_input_path <- "data/input_data/MOC"
CFUs_input_path <- "data/dataframes"

# Create subfolders for output files
dataframes_folder <- "data/dataframes"
if (!file.exists("data/dataframes")) {
  dir.create("data/dataframes", recursive = TRUE)
}
outputs_folder <- "data/outputs/MOC"
if (!file.exists("data/outputs/MOC")) {
  dir.create("data/outputs/MOC", recursive = TRUE)
}

# Load data and metadata
HNOCells <- read_csv(file.path(MOC_input_path, "MOC_HNOBac_2024.csv")) 
CFUs <- read_rds(file.path(CFUs_input_path, "CFU_values.rds")) 

Cells per HNO well

HNOCells$Date <- as.factor(HNOCells$Date)
HNOCells$Line <- as.factor(HNOCells$Line)
HNOCells <- HNOCells  %>%
  mutate(Line = factor(Line, levels = c("HNO918", "HNO204", "HNO919"))) 

HNOCells_summary <- HNOCells %>%
  group_by(Line) %>%
  summarize(median_cells = median(Cells_HNO),
            Q1 = quantile(Cells_HNO, 0.25),
            Q3 = quantile(Cells_HNO, 0.75),
            n = n(),
            SE = sd(Cells_HNO)/sqrt(n),
            CI = SE*1.96)

Plot

boxplot_HNOCells <- ggplot() +
  geom_boxplot(data = HNOCells, aes(x = Line, y = Cells_HNO)) +
  
  geom_jitter(data = HNOCells, aes(x = Line, y = Cells_HNO, shape = Line), 
             fill = "#5b5b5b", color = "#5b5b5b", size = 2, width = 0.15, alpha = 0.75) +
  
  scale_shape_manual(values = c(21, 22, 24)) +
  scale_y_log10(limits = c(100000, 10000000),
                labels = trans_format("log10", math_format(10^.x))) +
  labs(x = "HNO Line",
       y = "HNO Cells/Well") +
  theme_bw() +
  theme(panel.grid = element_blank(),
        text = element_text(size = 20), 
        axis.text.x = element_text(color = "black"),
        axis.text.y = element_text(color = "black"))
boxplot_HNOCells

Saving files

ggsave(boxplot_HNOCells, filename = paste0(outputs_folder, "/boxplot_HNOCells.png"), width = 12, height = 10)
saveRDS(boxplot_HNOCells, file.path(outputs_folder, paste0("boxplot_HNOCells.rds")))

# Save data frames as CSV files in the metadata folder
write_csv(HNOCells, file.path(dataframes_folder, "HNOCells_values.csv"))

# Save data frames as R objects in the metadata folder
saveRDS(HNOCells, file.path(dataframes_folder, "HNOCells_values.rds"))

# Use this to read the final objects
HNOCells <- readRDS("data/dataframes/HNOCells_values.rds")

Multiplicity of Colonization

CFU_Inoculum <- CFUs %>%
  filter(Time == "0") %>%
  group_by(Line, bacteria) %>%
  summarize(median_CFUs = median(NewCFU))

CFU_Inoculum <- left_join(CFU_Inoculum, HNOCells_summary, by = "Line")

MOC_Calcs <- CFU_Inoculum %>%
  mutate(MOC = (median_CFUs/median_cells)) %>%
  group_by(bacteria) %>%
  mutate(avg_MOC = (mean(MOC)))

Plot

boxplot_MOC <- ggplot() +
  geom_boxplot(data = MOC_Calcs, aes(x = bacteria, y = MOC, fill = bacteria)) +
  
  geom_jitter(data = MOC_Calcs, aes(x = bacteria, y = MOC, shape = Line), 
              fill = "grey50", color = "grey30", size = 2,  
              width = 0.001, stroke = 0.75) +
  scale_fill_manual(values = c("#2e67f2","#AA35E3","#927ed1")) +
  scale_shape_manual(values = c(21, 22, 24)) +
  labs(title = " ",
       x = "HNO Line",
       y = "MOC") +
  theme_bw() +
  theme(panel.grid = element_blank(), text = element_text(size = 30), 
        axis.text.x = element_markdown(), axis.text.y = element_text(color = "black"))
boxplot_MOC

Saving files

ggsave(boxplot_MOC, filename = paste0(outputs_folder, "/boxplot_MOC.png"), width = 12, height = 10)
saveRDS(boxplot_MOC, file.path(outputs_folder, paste0("boxplot_MOC.rds")))

# Save data frames as CSV files in the outputs folder
write_csv(MOC_Calcs, file.path(outputs_folder, "MOC_Calcs.csv"))

# Save data frames as R objects in the outputs folder
saveRDS(MOC_Calcs, file.path(outputs_folder, "MOC_Calcs.rds"))

# Use this to read the final objects
MOC_Calcs <- readRDS(file.path(outputs_folder, paste0("MOC_Calcs.rds")))